709 citations · 2.5k across the 39 of their papers we have counts for
12 papers · 1 filter
On Effective Parallelization of Monte Carlo Tree Search
Anji Liu, Yitao Liang, Ji Liu +2
Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construc…
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation
Yu Liu, Li Deng, Jianshu Chen +1
Many important classification problems, such as object classification, speech recognition, and machine translation, have been tackled by the supervised learning paradigm in the pas…
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li +5
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades. The…
A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification
Yue Zhao, Jianshu Chen, H. Vincent Poor
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hyp…
Stochastic Variance Reduction Methods for Policy Evaluation
Simon S. Du, Jianshu Chen, Lihong Li +2
Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy. In this…
Unsupervised Sequence Classification using Sequential Output Statistics
Yu Liu, Jianshu Chen, Li Deng
We consider learning a sequence classifier without labeled data by using sequential output statistics. The problem is highly valuable since obtaining labels in training data is oft…
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar +3
Many practical environments contain catastrophic states that an optimal agent would visit infrequently or never. Even on toy problems, Deep Reinforcement Learning (DRL) agents tend…
Unsupervised Learning of Predictors from Unpaired Input-Output Samples
Jianshu Chen, Po-Sen Huang, Xiaodong He +2
Unsupervised learning is the most challenging problem in machine learning and especially in deep learning. Among many scenarios, we study an unsupervised learning problem of high e…
Recurrent Reinforcement Learning: A Hybrid Approach
Xiujun Li, Lihong Li, Jianfeng Gao +4
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and…
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Jianshu Chen, Ji He, Yelong Shen +5
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior pro…
Dictionary Learning over Distributed Models
Jianshu Chen, Zaid J. Towfic, Ali H. Sayed
In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is rele…
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
Jianshu Chen, Li Deng
We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor. The RNN is equipped with both causal tempo…